Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations7344
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory289.0 B

Variable types

Numeric14
DateTime2
Unsupported1
Categorical2

Alerts

AH is highly overall correlated with PT08.S4(NO2) and 1 other fieldsHigh correlation
C6H6(GT) is highly overall correlated with CO(GT) and 7 other fieldsHigh correlation
CO(GT) is highly overall correlated with C6H6(GT) and 7 other fieldsHigh correlation
Hour is highly overall correlated with Is_DayHigh correlation
Is_Day is highly overall correlated with HourHigh correlation
NO2(GT) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
NOx(GT) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with C6H6(GT) and 7 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with C6H6(GT) and 7 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with C6H6(GT) and 7 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with AH and 7 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with C6H6(GT) and 7 other fieldsHigh correlation
RH is highly overall correlated with THigh correlation
T is highly overall correlated with AH and 2 other fieldsHigh correlation
Datetime has unique values Unique
Month is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hour has 314 (4.3%) zeros Zeros

Reproduction

Analysis started2025-05-21 16:56:06.200550
Analysis finished2025-05-21 16:56:44.840916
Duration38.64 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CO(GT)
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1297113
Minimum0.1
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:44.980176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.1
median1.8
Q32.8
95-th percentile4.9
Maximum11.9
Range11.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.4364715
Coefficient of variation (CV)0.6744912
Kurtosis2.6579751
Mean2.1297113
Median Absolute Deviation (MAD)0.8
Skewness1.3573969
Sum15640.6
Variance2.0634505
MonotonicityNot monotonic
2025-05-21T22:26:45.224262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 287
 
3.9%
1.4 269
 
3.7%
1.5 265
 
3.6%
1.6 264
 
3.6%
0.7 252
 
3.4%
1.1 251
 
3.4%
1.3 248
 
3.4%
0.8 243
 
3.3%
0.9 241
 
3.3%
1.2 239
 
3.3%
Other values (84) 4785
65.2%
ValueCountFrequency (%)
0.1 33
 
0.4%
0.2 45
 
0.6%
0.3 97
 
1.3%
0.4 160
2.2%
0.5 217
3.0%
0.6 238
3.2%
0.7 252
3.4%
0.8 243
3.3%
0.9 241
3.3%
1 287
3.9%
ValueCountFrequency (%)
11.9 1
 
< 0.1%
11.5 1
 
< 0.1%
10.2 2
< 0.1%
10.1 1
 
< 0.1%
9.9 1
 
< 0.1%
9.5 1
 
< 0.1%
9.4 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 1
 
< 0.1%
8.7 3
< 0.1%

PT08.S1(CO)
Real number (ℝ)

High correlation 

Distinct1022
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1110.5807
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:45.480772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile817
Q1946
median1075
Q31246
95-th percentile1515
Maximum2040
Range1393
Interquartile range (IQR)300

Descriptive statistics

Standard deviation218.68133
Coefficient of variation (CV)0.19690719
Kurtosis0.29374821
Mean1110.5807
Median Absolute Deviation (MAD)145
Skewness0.72745
Sum8156105
Variance47821.525
MonotonicityNot monotonic
2025-05-21T22:26:45.722973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
973 26
 
0.4%
1100 24
 
0.3%
938 23
 
0.3%
988 23
 
0.3%
966 22
 
0.3%
970 21
 
0.3%
969 21
 
0.3%
1016 21
 
0.3%
1009 21
 
0.3%
986 20
 
0.3%
Other values (1012) 7122
97.0%
ValueCountFrequency (%)
647 1
 
< 0.1%
649 1
 
< 0.1%
655 1
 
< 0.1%
667 3
< 0.1%
669 1
 
< 0.1%
676 1
 
< 0.1%
678 1
 
< 0.1%
679 1
 
< 0.1%
683 2
< 0.1%
689 2
< 0.1%
ValueCountFrequency (%)
2040 1
< 0.1%
2008 1
< 0.1%
1982 1
< 0.1%
1975 1
< 0.1%
1973 1
< 0.1%
1961 1
< 0.1%
1956 1
< 0.1%
1934 1
< 0.1%
1918 1
< 0.1%
1917 1
< 0.1%

NMHC(GT)
Real number (ℝ)

Distinct427
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-150.2244
Minimum-200
Maximum1189
Zeros0
Zeros (%)0.0%
Negative6481
Negative (%)88.2%
Memory size57.5 KiB
2025-05-21T22:26:46.013473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q1-200
median-200
Q3-200
95-th percentile185
Maximum1189
Range1389
Interquartile range (IQR)0

Descriptive statistics

Standard deviation153.78833
Coefficient of variation (CV)-1.023724
Kurtosis14.91144
Mean-150.2244
Median Absolute Deviation (MAD)0
Skewness3.6512168
Sum-1103248
Variance23650.849
MonotonicityNot monotonic
2025-05-21T22:26:46.242915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 6481
88.2%
66 13
 
0.2%
88 8
 
0.1%
93 8
 
0.1%
84 7
 
0.1%
60 7
 
0.1%
95 7
 
0.1%
29 7
 
0.1%
57 7
 
0.1%
40 6
 
0.1%
Other values (417) 793
 
10.8%
ValueCountFrequency (%)
-200 6481
88.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 2
 
< 0.1%
18 2
 
< 0.1%
19 2
 
< 0.1%
ValueCountFrequency (%)
1189 1
< 0.1%
1129 1
< 0.1%
1084 1
< 0.1%
1042 1
< 0.1%
974 1
< 0.1%
926 1
< 0.1%
899 1
< 0.1%
880 1
< 0.1%
872 1
< 0.1%
840 1
< 0.1%

C6H6(GT)
Real number (ℝ)

High correlation 

Distinct393
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.275735
Minimum0.2
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:46.443022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.8
Q14.6
median8.5
Q314.3
95-th percentile24.6
Maximum63.7
Range63.5
Interquartile range (IQR)9.7

Descriptive statistics

Standard deviation7.4410677
Coefficient of variation (CV)0.72413968
Kurtosis2.4746683
Mean10.275735
Median Absolute Deviation (MAD)4.5
Skewness1.3283572
Sum75465
Variance55.369489
MonotonicityNot monotonic
2025-05-21T22:26:46.658359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 74
 
1.0%
2.8 66
 
0.9%
3.8 64
 
0.9%
4 61
 
0.8%
5.3 61
 
0.8%
3.1 61
 
0.8%
2.5 61
 
0.8%
2.4 59
 
0.8%
6 58
 
0.8%
6.7 57
 
0.8%
Other values (383) 6722
91.5%
ValueCountFrequency (%)
0.2 5
 
0.1%
0.3 7
 
0.1%
0.4 8
 
0.1%
0.5 14
0.2%
0.6 18
0.2%
0.7 26
0.4%
0.8 17
0.2%
0.9 15
0.2%
1 24
0.3%
1.1 17
0.2%
ValueCountFrequency (%)
63.7 1
< 0.1%
52.1 1
< 0.1%
50.8 1
< 0.1%
50.7 1
< 0.1%
50.6 1
< 0.1%
49.5 1
< 0.1%
49.4 1
< 0.1%
48.2 1
< 0.1%
47.7 1
< 0.1%
47.5 1
< 0.1%

PT08.S2(NMHC)
Real number (ℝ)

High correlation 

Distinct1197
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947.19812
Minimum387
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:46.896079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum387
5-th percentile568
Q1743
median919
Q31125.25
95-th percentile1419.85
Maximum2214
Range1827
Interquartile range (IQR)382.25

Descriptive statistics

Standard deviation265.47161
Coefficient of variation (CV)0.28027041
Kurtosis0.033238285
Mean947.19812
Median Absolute Deviation (MAD)190
Skewness0.53194129
Sum6956223
Variance70475.175
MonotonicityNot monotonic
2025-05-21T22:26:47.122756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880 20
 
0.3%
776 19
 
0.3%
800 19
 
0.3%
709 18
 
0.2%
945 18
 
0.2%
859 17
 
0.2%
687 17
 
0.2%
701 17
 
0.2%
896 17
 
0.2%
924 17
 
0.2%
Other values (1187) 7165
97.6%
ValueCountFrequency (%)
387 1
< 0.1%
390 1
< 0.1%
397 1
< 0.1%
402 2
< 0.1%
407 2
< 0.1%
408 1
< 0.1%
409 1
< 0.1%
412 1
< 0.1%
415 1
< 0.1%
417 1
< 0.1%
ValueCountFrequency (%)
2214 1
< 0.1%
2007 1
< 0.1%
1983 1
< 0.1%
1981 1
< 0.1%
1980 1
< 0.1%
1959 1
< 0.1%
1958 1
< 0.1%
1935 1
< 0.1%
1924 1
< 0.1%
1920 1
< 0.1%

NOx(GT)
Real number (ℝ)

High correlation 

Distinct897
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.08156
Minimum-200
Maximum1479
Zeros0
Zeros (%)0.0%
Negative400
Negative (%)5.4%
Memory size57.5 KiB
2025-05-21T22:26:47.310847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q190
median176
Q3320
95-th percentile677.85
Maximum1479
Range1679
Interquartile range (IQR)230

Descriptive statistics

Standard deviation227.14402
Coefficient of variation (CV)1.0046994
Kurtosis2.390654
Mean226.08156
Median Absolute Deviation (MAD)104
Skewness1.1038088
Sum1660343
Variance51594.406
MonotonicityNot monotonic
2025-05-21T22:26:47.499902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 400
 
5.4%
89 35
 
0.5%
180 33
 
0.4%
132 33
 
0.4%
65 33
 
0.4%
41 32
 
0.4%
51 32
 
0.4%
95 32
 
0.4%
122 31
 
0.4%
166 31
 
0.4%
Other values (887) 6652
90.6%
ValueCountFrequency (%)
-200 400
5.4%
2 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 4
 
0.1%
12 3
 
< 0.1%
13 4
 
0.1%
ValueCountFrequency (%)
1479 1
< 0.1%
1389 2
< 0.1%
1369 1
< 0.1%
1358 1
< 0.1%
1345 1
< 0.1%
1301 1
< 0.1%
1290 1
< 0.1%
1247 1
< 0.1%
1230 1
< 0.1%
1220 1
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

High correlation 

Distinct1166
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean826.92007
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:47.687981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile477
Q1649
median795
Q3960
95-th percentile1282
Maximum2683
Range2361
Interquartile range (IQR)311

Descriptive statistics

Standard deviation256.64843
Coefficient of variation (CV)0.31036667
Kurtosis2.9168153
Mean826.92007
Median Absolute Deviation (MAD)154
Skewness1.1515821
Sum6072901
Variance65868.416
MonotonicityNot monotonic
2025-05-21T22:26:47.919644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702 21
 
0.3%
846 21
 
0.3%
891 21
 
0.3%
733 20
 
0.3%
705 20
 
0.3%
765 20
 
0.3%
800 19
 
0.3%
748 19
 
0.3%
685 18
 
0.2%
631 18
 
0.2%
Other values (1156) 7147
97.3%
ValueCountFrequency (%)
322 1
< 0.1%
325 2
< 0.1%
328 1
< 0.1%
330 2
< 0.1%
334 1
< 0.1%
335 1
< 0.1%
340 1
< 0.1%
341 1
< 0.1%
345 1
< 0.1%
346 1
< 0.1%
ValueCountFrequency (%)
2683 1
< 0.1%
2559 1
< 0.1%
2542 1
< 0.1%
2327 1
< 0.1%
2318 1
< 0.1%
2294 1
< 0.1%
2121 1
< 0.1%
2095 2
< 0.1%
2081 1
< 0.1%
2077 1
< 0.1%

NO2(GT)
Real number (ℝ)

High correlation 

Distinct275
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.650327
Minimum-200
Maximum333
Zeros0
Zeros (%)0.0%
Negative403
Negative (%)5.5%
Memory size57.5 KiB
2025-05-21T22:26:48.100899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q173
median107
Q3139
95-th percentile198
Maximum333
Range533
Interquartile range (IQR)66

Descriptive statistics

Standard deviation85.089998
Coefficient of variation (CV)0.88039018
Kurtosis5.5710917
Mean96.650327
Median Absolute Deviation (MAD)34
Skewness-2.0437373
Sum709800
Variance7240.3078
MonotonicityNot monotonic
2025-05-21T22:26:48.341467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 403
 
5.5%
110 72
 
1.0%
119 72
 
1.0%
114 71
 
1.0%
117 71
 
1.0%
95 70
 
1.0%
116 68
 
0.9%
97 67
 
0.9%
115 67
 
0.9%
107 67
 
0.9%
Other values (265) 6316
86.0%
ValueCountFrequency (%)
-200 403
5.5%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
333 1
 
< 0.1%
322 1
 
< 0.1%
310 1
 
< 0.1%
309 1
 
< 0.1%
306 1
 
< 0.1%
301 1
 
< 0.1%
295 1
 
< 0.1%
288 2
< 0.1%
285 1
 
< 0.1%
283 3
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

High correlation 

Distinct1552
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1444.7527
Minimum551
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:48.607041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile876
Q11203
median1447
Q31673
95-th percentile2024
Maximum2775
Range2224
Interquartile range (IQR)470

Descriptive statistics

Standard deviation350.34417
Coefficient of variation (CV)0.24249421
Kurtosis-0.031493036
Mean1444.7527
Median Absolute Deviation (MAD)235
Skewness0.21912674
Sum10610264
Variance122741.04
MonotonicityNot monotonic
2025-05-21T22:26:48.803137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1488 19
 
0.3%
1490 17
 
0.2%
1580 17
 
0.2%
1418 16
 
0.2%
1539 16
 
0.2%
1257 15
 
0.2%
1467 15
 
0.2%
1405 15
 
0.2%
1638 15
 
0.2%
1374 15
 
0.2%
Other values (1542) 7184
97.8%
ValueCountFrequency (%)
551 1
< 0.1%
559 1
< 0.1%
561 1
< 0.1%
579 1
< 0.1%
601 1
< 0.1%
602 1
< 0.1%
605 1
< 0.1%
621 1
< 0.1%
637 1
< 0.1%
640 1
< 0.1%
ValueCountFrequency (%)
2775 1
< 0.1%
2746 1
< 0.1%
2691 1
< 0.1%
2679 1
< 0.1%
2667 1
< 0.1%
2665 1
< 0.1%
2643 1
< 0.1%
2641 2
< 0.1%
2622 1
< 0.1%
2609 1
< 0.1%

PT08.S5(O3)
Real number (ℝ)

High correlation 

Distinct1707
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1043.5129
Minimum221
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:49.019075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile465
Q1744.75
median990
Q31305
95-th percentile1790.7
Maximum2523
Range2302
Interquartile range (IQR)560.25

Descriptive statistics

Standard deviation405.56961
Coefficient of variation (CV)0.38865796
Kurtosis0.00315036
Mean1043.5129
Median Absolute Deviation (MAD)276
Skewness0.57698641
Sum7663559
Variance164486.71
MonotonicityNot monotonic
2025-05-21T22:26:49.247936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
836 18
 
0.2%
825 17
 
0.2%
826 16
 
0.2%
807 15
 
0.2%
926 15
 
0.2%
905 15
 
0.2%
949 14
 
0.2%
816 14
 
0.2%
1059 14
 
0.2%
1019 13
 
0.2%
Other values (1697) 7193
97.9%
ValueCountFrequency (%)
221 1
< 0.1%
225 1
< 0.1%
227 1
< 0.1%
232 1
< 0.1%
252 1
< 0.1%
257 1
< 0.1%
261 2
< 0.1%
262 1
< 0.1%
263 1
< 0.1%
268 2
< 0.1%
ValueCountFrequency (%)
2523 1
< 0.1%
2522 1
< 0.1%
2519 1
< 0.1%
2515 1
< 0.1%
2494 1
< 0.1%
2480 1
< 0.1%
2475 1
< 0.1%
2465 1
< 0.1%
2452 1
< 0.1%
2434 1
< 0.1%

T
Real number (ℝ)

High correlation 

Distinct431
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.770425
Minimum-1.9
Maximum44.6
Zeros1
Zeros (%)< 0.1%
Negative12
Negative (%)0.2%
Memory size57.5 KiB
2025-05-21T22:26:49.435383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.9
5-th percentile4.3
Q111.2
median16.9
Q323.8
95-th percentile34.2
Maximum44.6
Range46.5
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation8.8626883
Coefficient of variation (CV)0.4987325
Kurtosis-0.41689262
Mean17.770425
Median Absolute Deviation (MAD)6.3
Skewness0.37743379
Sum130506
Variance78.547244
MonotonicityNot monotonic
2025-05-21T22:26:49.694359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 45
 
0.6%
20.8 45
 
0.6%
12 44
 
0.6%
21.3 43
 
0.6%
13.5 43
 
0.6%
15.6 41
 
0.6%
12.3 40
 
0.5%
13.8 40
 
0.5%
13.4 40
 
0.5%
13.1 39
 
0.5%
Other values (421) 6924
94.3%
ValueCountFrequency (%)
-1.9 1
< 0.1%
-1.3 2
< 0.1%
-1.2 1
< 0.1%
-1.1 1
< 0.1%
-0.6 2
< 0.1%
-0.5 1
< 0.1%
-0.3 1
< 0.1%
-0.2 1
< 0.1%
-0.1 2
< 0.1%
0 1
< 0.1%
ValueCountFrequency (%)
44.6 1
< 0.1%
44.3 1
< 0.1%
43.4 1
< 0.1%
43.1 1
< 0.1%
42.8 2
< 0.1%
42.6 1
< 0.1%
42.5 1
< 0.1%
42.2 2
< 0.1%
42 1
< 0.1%
41.9 2
< 0.1%

RH
Real number (ℝ)

High correlation 

Distinct744
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.060076
Minimum9.2
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:49.893256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile20.2
Q135.4
median49.3
Q362.5
95-th percentile78
Maximum88.7
Range79.5
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation17.451563
Coefficient of variation (CV)0.35571822
Kurtosis-0.84387639
Mean49.060076
Median Absolute Deviation (MAD)13.5
Skewness-0.014976348
Sum360297.2
Variance304.55705
MonotonicityNot monotonic
2025-05-21T22:26:50.130438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.1 27
 
0.4%
47.8 26
 
0.4%
45.9 24
 
0.3%
57.9 23
 
0.3%
50.8 22
 
0.3%
50.9 21
 
0.3%
50.1 21
 
0.3%
39.4 21
 
0.3%
43.4 21
 
0.3%
43 21
 
0.3%
Other values (734) 7117
96.9%
ValueCountFrequency (%)
9.2 1
< 0.1%
9.3 1
< 0.1%
9.6 1
< 0.1%
9.8 1
< 0.1%
9.9 2
< 0.1%
10.2 1
< 0.1%
10.4 1
< 0.1%
11.1 1
< 0.1%
11.6 1
< 0.1%
11.8 1
< 0.1%
ValueCountFrequency (%)
88.7 1
 
< 0.1%
87.1 1
 
< 0.1%
87 1
 
< 0.1%
86.6 1
 
< 0.1%
86.5 2
< 0.1%
86 1
 
< 0.1%
85.7 3
< 0.1%
85.6 1
 
< 0.1%
85.5 1
 
< 0.1%
85.4 2
< 0.1%

AH
Real number (ℝ)

High correlation 

Distinct5719
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98945331
Minimum0.1847
Maximum2.1806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-21T22:26:50.374946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1847
5-th percentile0.388415
Q10.6981
median0.9597
Q31.2586
95-th percentile1.70597
Maximum2.1806
Range1.9959
Interquartile range (IQR)0.5605

Descriptive statistics

Standard deviation0.3998891
Coefficient of variation (CV)0.40415156
Kurtosis-0.45978978
Mean0.98945331
Median Absolute Deviation (MAD)0.2771
Skewness0.34040594
Sum7266.5451
Variance0.15991129
MonotonicityNot monotonic
2025-05-21T22:26:50.606612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7487 6
 
0.1%
1.1199 5
 
0.1%
0.8736 5
 
0.1%
0.8394 5
 
0.1%
0.6686 5
 
0.1%
0.9722 5
 
0.1%
1.5115 4
 
0.1%
1.3208 4
 
0.1%
0.7706 4
 
0.1%
0.8193 4
 
0.1%
Other values (5709) 7297
99.4%
ValueCountFrequency (%)
0.1847 1
< 0.1%
0.1862 1
< 0.1%
0.191 1
< 0.1%
0.1975 1
< 0.1%
0.1988 1
< 0.1%
0.2029 1
< 0.1%
0.2031 1
< 0.1%
0.2062 1
< 0.1%
0.2086 1
< 0.1%
0.2157 1
< 0.1%
ValueCountFrequency (%)
2.1806 1
< 0.1%
2.1719 1
< 0.1%
2.1395 1
< 0.1%
2.1362 1
< 0.1%
2.1247 1
< 0.1%
2.1195 1
< 0.1%
2.117 1
< 0.1%
2.1164 1
< 0.1%
2.1144 1
< 0.1%
2.1107 1
< 0.1%

Datetime
Date

Unique 

Distinct7344
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size57.5 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-21T22:26:51.388673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:51.608378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hour
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.699619
Minimum0
Maximum23
Zeros314
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2025-05-21T22:26:51.808960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9070536
Coefficient of variation (CV)0.5903657
Kurtosis-1.1779536
Mean11.699619
Median Absolute Deviation (MAD)6
Skewness-0.050672614
Sum85922
Variance47.707389
MonotonicityNot monotonic
2025-05-21T22:26:51.984810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20 317
 
4.3%
18 316
 
4.3%
21 316
 
4.3%
19 316
 
4.3%
9 315
 
4.3%
10 315
 
4.3%
22 314
 
4.3%
0 314
 
4.3%
16 314
 
4.3%
13 314
 
4.3%
Other values (14) 4193
57.1%
ValueCountFrequency (%)
0 314
4.3%
1 309
4.2%
2 307
4.2%
3 300
4.1%
4 172
2.3%
5 306
4.2%
6 308
4.2%
7 309
4.2%
8 308
4.2%
9 315
4.3%
ValueCountFrequency (%)
23 313
4.3%
22 314
4.3%
21 316
4.3%
20 317
4.3%
19 316
4.3%
18 316
4.3%
17 313
4.3%
16 314
4.3%
15 311
4.2%
14 311
4.2%

Month
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size57.5 KiB

Weekday
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size459.9 KiB
Sunday
1118 
Monday
1116 
Friday
1068 
Saturday
1034 
Thursday
1027 
Other values (2)
1981 

Length

Max length9
Median length8
Mean length7.1074346
Min length6

Characters and Unicode

Total characters52197
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowSunday
4th rowSunday
5th rowSunday

Common Values

ValueCountFrequency (%)
Sunday 1118
15.2%
Monday 1116
15.2%
Friday 1068
14.5%
Saturday 1034
14.1%
Thursday 1027
14.0%
Wednesday 1015
13.8%
Tuesday 966
13.2%

Length

2025-05-21T22:26:52.144367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T22:26:52.375404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sunday 1118
15.2%
monday 1116
15.2%
friday 1068
14.5%
saturday 1034
14.1%
thursday 1027
14.0%
wednesday 1015
13.8%
tuesday 966
13.2%

Most occurring characters

ValueCountFrequency (%)
a 8378
16.1%
d 8359
16.0%
y 7344
14.1%
u 4145
7.9%
n 3249
 
6.2%
r 3129
 
6.0%
s 3008
 
5.8%
e 2996
 
5.7%
S 2152
 
4.1%
T 1993
 
3.8%
Other values (7) 7444
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44853
85.9%
Uppercase Letter 7344
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8378
18.7%
d 8359
18.6%
y 7344
16.4%
u 4145
9.2%
n 3249
 
7.2%
r 3129
 
7.0%
s 3008
 
6.7%
e 2996
 
6.7%
o 1116
 
2.5%
i 1068
 
2.4%
Other values (2) 2061
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 2152
29.3%
T 1993
27.1%
M 1116
15.2%
F 1068
14.5%
W 1015
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 52197
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8378
16.1%
d 8359
16.0%
y 7344
14.1%
u 4145
7.9%
n 3249
 
6.2%
r 3129
 
6.0%
s 3008
 
5.8%
e 2996
 
5.7%
S 2152
 
4.1%
T 1993
 
3.8%
Other values (7) 7444
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8378
16.1%
d 8359
16.0%
y 7344
14.1%
u 4145
7.9%
n 3249
 
6.2%
r 3129
 
6.0%
s 3008
 
5.8%
e 2996
 
5.7%
S 2152
 
4.1%
T 1993
 
3.8%
Other values (7) 7444
14.3%
Distinct347
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size287.0 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-21T22:26:52.568620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:52.771659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Is_Day
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.9 KiB
Day
4060 
Night
3284 

Length

Max length5
Median length3
Mean length3.8943355
Min length3

Characters and Unicode

Total characters28600
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDay
2nd rowNight
3rd rowNight
4th rowNight
5th rowNight

Common Values

ValueCountFrequency (%)
Day 4060
55.3%
Night 3284
44.7%

Length

2025-05-21T22:26:53.000827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T22:26:53.136920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
day 4060
55.3%
night 3284
44.7%

Most occurring characters

ValueCountFrequency (%)
D 4060
14.2%
a 4060
14.2%
y 4060
14.2%
N 3284
11.5%
i 3284
11.5%
g 3284
11.5%
h 3284
11.5%
t 3284
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21256
74.3%
Uppercase Letter 7344
 
25.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4060
19.1%
y 4060
19.1%
i 3284
15.4%
g 3284
15.4%
h 3284
15.4%
t 3284
15.4%
Uppercase Letter
ValueCountFrequency (%)
D 4060
55.3%
N 3284
44.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 28600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 4060
14.2%
a 4060
14.2%
y 4060
14.2%
N 3284
11.5%
i 3284
11.5%
g 3284
11.5%
h 3284
11.5%
t 3284
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 4060
14.2%
a 4060
14.2%
y 4060
14.2%
N 3284
11.5%
i 3284
11.5%
g 3284
11.5%
h 3284
11.5%
t 3284
11.5%

Interactions

2025-05-21T22:26:41.699719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.183594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.472648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:12.680785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.071421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:17.668924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:20.294705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:23.770263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:26.533326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.863046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:31.333301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.726451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:36.173143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:39.168653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:41.843111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.357468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.625612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:12.812500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.263043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:17.844146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:20.486809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:23.938098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:26.668698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.051048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:31.478641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.913623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:36.923356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:39.353060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.049385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.508165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.824960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.027142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.436423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.050105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:20.691525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:24.108844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:26.853765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.163192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:31.730269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.039673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.107515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:39.542234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.212800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.644554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.940908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.234081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.593462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.183015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:20.843183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:24.299134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.028178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.340196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:31.903339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.173178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.273399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:39.796147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.417853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.839964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.094013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.393362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.783425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.307112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:21.047429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:24.503769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.180986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.546501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.077341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.395128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.443138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:39.993078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.616624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:08.997139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.218620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.572858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:15.970151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.564430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:21.234837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:24.671606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.413825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.709940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.257962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.570882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.623034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.161541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.795534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.170316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.341635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.709009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:16.155221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.715240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:21.408571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:24.864879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.537443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:29.883110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.463576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.745568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.837396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.279796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:42.912813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.315939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.495143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.862895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:16.292197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:18.943039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:21.585779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:25.053150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.691693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.040886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.593301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:34.910437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:37.972214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.428149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:43.076019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.501776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.710630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:13.982908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:16.477915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:19.141618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:21.789295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:25.271082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:27.879918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.187386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.771809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:35.064127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.168887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.603043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:43.253281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.695170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.846870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:14.149687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:16.697866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:19.371107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:22.868121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:25.490548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.098293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.361527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:32.918132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:35.302074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.345322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.796164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:43.452248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.832817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:11.996211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:14.305976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:16.869861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:19.533857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:22.990137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:25.703057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.232914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.567875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.054672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:35.483377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.502066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:40.902019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:43.631717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:09.980272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:12.182786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:14.492977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:17.039418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:19.783703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:23.116560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:25.905159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.383563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.759404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.213360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:35.627454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.643513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:41.080464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:43.838764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.163027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:12.380094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:14.671604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:17.255919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:19.953957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:23.308006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:26.120012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.545650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:30.964688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.391976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:35.833027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.803104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:41.264675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:44.009684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:10.372105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:12.566190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:14.882951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:17.448622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:20.142857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:23.530298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:26.343224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:28.714980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:31.173786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:33.612903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:36.047337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:38.998224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-21T22:26:41.500731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-21T22:26:53.286193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AHC6H6(GT)CO(GT)HourIs_DayNMHC(GT)NO2(GT)NOx(GT)PT08.S1(CO)PT08.S2(NMHC)PT08.S3(NOx)PT08.S4(NO2)PT08.S5(O3)RHTWeekday
AH1.0000.2030.053-0.0130.051-0.151-0.322-0.1830.1450.203-0.2010.6650.0850.1630.7090.102
C6H6(GT)0.2031.0000.9270.4000.2330.0320.6550.6960.8861.000-0.8400.7430.874-0.0870.2600.093
CO(GT)0.0530.9271.0000.4120.1980.0720.7180.7630.8790.927-0.8110.5940.856-0.0060.0690.076
Hour-0.0130.4000.4121.0000.9560.0130.4010.3030.3320.400-0.3010.2270.237-0.2840.1840.000
Is_Day0.0510.2330.1980.9561.0000.1150.2400.2590.2030.2460.1810.1560.1220.2940.2270.000
NMHC(GT)-0.1510.0320.0720.0130.1151.000-0.061-0.1460.1490.0320.1860.1600.0160.003-0.0820.051
NO2(GT)-0.3220.6550.7180.4010.240-0.0611.0000.8520.6600.655-0.6650.1570.686-0.108-0.2000.075
NOx(GT)-0.1830.6960.7630.3030.259-0.1460.8521.0000.7100.696-0.7680.1770.7660.163-0.2650.065
PT08.S1(CO)0.1450.8860.8790.3320.2030.1490.6600.7101.0000.886-0.8510.6350.8970.1360.0600.076
PT08.S2(NMHC)0.2031.0000.9270.4000.2460.0320.6550.6960.8861.000-0.8400.7430.874-0.0870.2600.099
PT08.S3(NOx)-0.201-0.840-0.811-0.3010.1810.186-0.665-0.768-0.851-0.8401.000-0.509-0.863-0.131-0.0790.086
PT08.S4(NO2)0.6650.7430.5940.2270.1560.1600.1570.1770.6350.743-0.5091.0000.546-0.0490.6210.094
PT08.S5(O3)0.0850.8740.8560.2370.1220.0160.6860.7660.8970.874-0.8630.5461.0000.159-0.0210.088
RH0.163-0.087-0.006-0.2840.2940.003-0.1080.1630.136-0.087-0.131-0.0490.1591.000-0.5320.051
T0.7090.2600.0690.1840.227-0.082-0.200-0.2650.0600.260-0.0790.621-0.021-0.5321.0000.054
Weekday0.1020.0930.0760.0000.0000.0510.0750.0650.0760.0990.0860.0940.0880.0510.0541.000

Missing values

2025-05-21T22:26:44.197997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-21T22:26:44.650539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHDatetimeHourMonthWeekdayDateOnlyIs_Day
02.6136015011.9104616610561131692126813.648.90.75782004-10-03 18:00:00182004-10Sunday2004-10-03Day
12.012921129.4955103117492155997213.347.70.72552004-10-03 19:00:00192004-10Sunday2004-10-03Night
22.21402889.093913111401141555107411.954.00.75022004-10-03 20:00:00202004-10Sunday2004-10-03Night
32.21376809.294817210921221584120311.060.00.78672004-10-03 21:00:00212004-10Sunday2004-10-03Night
41.61272516.583613112051161490111011.259.60.78882004-10-03 22:00:00222004-10Sunday2004-10-03Night
51.21197384.775089133796139394911.259.20.78482004-10-03 23:00:00232004-10Sunday2004-10-03Night
61.21185313.669062146277133373311.356.80.76032004-11-03 00:00:0002004-11Wednesday2004-11-03Night
71.01136313.367262145376133373010.760.00.77022004-11-03 01:00:0012004-11Wednesday2004-11-03Night
80.91094242.360945157960127662010.759.70.76482004-11-03 02:00:0022004-11Wednesday2004-11-03Night
90.61010191.7561-2001705-200123550110.360.20.75172004-11-03 03:00:0032004-11Wednesday2004-11-03Night
CO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHDatetimeHourMonthWeekdayDateOnlyIs_Day
73340.5888-2001.35287710775398757810.459.90.75502005-04-04 05:00:0052005-04Monday2005-04-04Night
73351.11031-2004.47301827609311299059.563.10.75312005-04-04 06:00:0062005-04Monday2005-04-04Day
73364.01384-20017.41221594470155160014579.761.90.74462005-04-04 07:00:0072005-04Monday2005-04-04Day
73375.01446-20022.413625864151741777170513.548.90.75532005-04-04 08:00:0082005-04Monday2005-04-04Day
73383.91297-20013.611025235071871375158318.236.30.74872005-04-04 09:00:0092005-04Monday2005-04-04Day
73393.11314-20013.511014725391901374172921.929.30.75682005-04-04 10:00:00102005-04Monday2005-04-04Day
73402.41163-20011.410273536041791264126924.323.70.71192005-04-04 11:00:00112005-04Monday2005-04-04Day
73412.41142-20012.410632936031751241109226.918.30.64062005-04-04 12:00:00122005-04Monday2005-04-04Day
73422.11003-2009.5961235702156104177028.313.50.51392005-04-04 13:00:00132005-04Monday2005-04-04Day
73432.21071-20011.91047265654168112981628.513.10.50282005-04-04 14:00:00142005-04Monday2005-04-04Day